10 Best Practices for Managing Modern Data in Motion

Before big data and fast data, the challenge of data in motion was simple: move fields from fairly static databases to an appropriate home in a data warehouse, or move data between databases and apps in a standardized fashion. The process resembled a factory assembly line. In today’s world, consuming applications and routes and rules for moving data constantly change. Big data processing operations are more like a city traffic grid than the linear path taken by traditional data. The emerging world is many-to-many, with streaming or micro-batched data coming from numerous sources and being consumed by numerous applications. Because modern data is so dynamic, dealing with data in motion requires a full lifecycle perspective including day-to-day operations and agility over time. Organizations must tune the performance of their data movement system as both data infrastructure and business requirements for the use of data evolve.


Read the source article at eweek.com